- Storage - incorporating SSD and other new storage technologies into virtualized environments. Investigating latency aware designs in SSD provisioning for object stores. Integrating NVM into the storage and memory hierarcy of large systems.

We are seeking candidates who will be excited to work with systems research in a hands on manner. Our research draws on well designed empirical studies involving real systems at scale. We welcome candidates with prior industry experience, particularly in virtualization and storage systems development. A willingness to get down and dirty with code and data is a must.

2. Machine Learning on Graph Data to Detect Threats

No. of positions: One

We consider two parts to the problem of machine learning on graph data. The first is more or less based on static analysis of graph data (and its meta data).

Graph Summarization and Visualisation

The continuous rise in inter-connected data creates the need for summarizing large graphs to extract relevant information [3][4]. It is useful in various areas such as fraud detection (banking data), cyber-security, social network analysis and many more. Currently, visualisation of large graphs is almost impossible. The project aims to develop a framework for summarization and visualising large graphs, including analysis of the textual content that is available, for example, in social network graphs.
Pertinent questions are : How can we summarize large weighted, directed graphs ? How can we build big data, fast analysis systems to parse and understand large amounts of inter-connected data ?

Malware and Anomaly Detection using Machine Learning Techniques

In the last decade, there is an increase in malware infections propagated over the Internet. Anti-malware uses signature based techniques to prevent malware infections. However, with the recent rise in advanced persistent threats (APT), there is an increase in ‘unknown’ attacks making anti-malwares ineffective. There is a need for malware detection based on their behavior. Recent works have highlighted the benefit of using behavioral approach to identify malwares [1][2].
The project also aims to develop a machine learning based framework for the detection and classification using behaviour of the malwares. Identifying malwares using network logs and sensors at different parts of the networks.

3. Software Defined networking and related technologies

No. of positions: One or Two

We are looking for 1 or 2 RAP PhD candidate who will work on Software Defined networking and related technologies. The goal is to build a services framework in addition to a third party as well as in-house controller. The controller will interface with our home grown SDN whiteboxes using a REST API and modeled using YANG and NETCONF. The work involved extensive programming in Python and interfacing with hardware. Good computer architecture fundamentals are absolutely necessary. The candidate must also be able to work in a competitive environment which requires deadline based project deliverable.